URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision

In recent years, deep learning and computer vision have been applied to solve complex problems across many domains. In urban studies, these technologies have been instrumental in the development of smart cities and autonomous vehicles. However, a knowledge gap is present when it comes to informal urban regions in less developed countries. How can deep learning and artificial intelligence untangle the complexities of informality to advance urban modelling? In this paper, we introduce a framework for multipurpose realistic-dynamic urban modelling using deep convolutional neural networks. The purpose of the framework is twofold: (1) to sense and detect informality and slums in urban scenes from aerial and street-level images and (2) to detect pedestrian and transport modes. The model has been trained on images of urban scenes in cities across the globe. The framework shows strong validation performance in the identification of planned and unplanned regions, despite broad variations in the classified images. The algorithms of the URBAN-i model are coded in Python and the trained models can be applied to images of any urban setting, including informal settlements and slum regions.